Description Usage Arguments Value Examples

`PPforest`

implements a random forest using projection pursuit trees algorithm (based on PPtreeViz package).

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`data` |
Data frame with the complete data set. |

`class` |
A character with the name of the class variable. |

`std` |
if TRUE standardize the data set, needed to compute global importance measure. |

`size.tr` |
is the size proportion of the training if we want to split the data in training and test. |

`m` |
is the number of bootstrap replicates, this corresponds with the number of trees to grow. To ensure that each observation is predicted a few times we have to select this number no too small. |

`PPmethod` |
is the projection pursuit index to optimize in each classification tree. The options are |

`size.p` |
proportion of variables randomly sampled in each split. |

`lambda` |
penalty parameter in PDA index and is between 0 to 1 . If |

`parallel` |
logical condition, if it is TRUE then parallelize the function |

`cores` |
number of cores used in the parallelization |

`rule` |
split rule 1: mean of two group means 2: weighted mean of two group means - weight with group size 3: weighted mean of two group means - weight with group sd 4: weighted mean of two group means - weight with group se 5: mean of two group medians 6: weighted mean of two group medians - weight with group size 7: weighted mean of two group median - weight with group IQR 8: weighted mean of two group median - weight with group IQR and size |

An object of class `PPforest`

with components.

`prediction.training` |
predicted values for training data set. |

`training.error` |
error of the training data set. |

`prediction.test` |
predicted values for the test data set if |

`error.test` |
error of the test data set if |

`oob.error.forest` |
out of bag error in the forest. |

`oob.error.tree` |
out of bag error for each tree in the forest. |

`boot.samp` |
information of bootrap samples. |

`output.trees` |
output from a |

`proximity` |
Proximity matrix, if two cases are classified in the same terminal node then the proximity matrix is increased by one in |

`votes` |
a matrix with one row for each input data point and one column for each class, giving the fraction of (OOB) votes from the |

`n.tree` |
number of trees grown in |

`n.var` |
number of predictor variables selected to use for spliting at each node. |

`type` |
classification. |

`confusion` |
confusion matrix of the prediction (based on OOB data). |

`call` |
the original call to |

`train` |
is the training data based on |

`test` |
is the test data based on |

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